Amelie Grazing Experiment Metaanalysis

Setup

Load necessary packages

require(sciplot)

## Loading required package: sciplot

require(Rmisc)

## Loading required package: Rmisc

## Loading required package: lattice

## Loading required package: plyr

Set up plotting commands

# Colors from Hiroshige and Cassatt2, Metbrewer, https://github.com/BlakeRMills/MetBrewer
TAcol <- rgb(230/255,98/255,84/255) # TAcol will stand-in for the red cryptophyte
TAcol.bg <- rgb(230/255,98/255,84/255,.5)
HPcol <- rgb(114/255,187/255,213/255) # HPcol will stand in for the blue-green cryptophyte
HPcol.bg <- rgb(114/255,187/255,213/255,.5)
Bothcol <- rgb(144/255,113/255,158/255) # We'll use purple for both species
Bothcol.bg <- rgb(144/255,113/255,158/255,.5)


# Set up ordered lists of species
Mesos <- c('MR','MC')
Red.cryptos <- c('RS','SM','TA')
Blue.cryptos <- c('CM','HP')

# Setting cryptophyte name vectors 
Red.names <- c(expression(italic('R. salina')),expression(italic('S. major')),expression(italic('T. amph.')))
Blue.names <- c(expression(italic('C. meso.')),expression(italic('H. pacifica')))

# Bar density for hatched barplots
dens.list <- c(0,10,15)
angle.list <- c(0,45,135)

# Panel labels for subplots
panel.labs <- matrix(c('(a)','(b)','(c)','(d)','(e)','(f)'),nrow=2,ncol=3,byrow=T)

Data import

dat <- read.csv("/Users/hollyvm/GoogleSync/StudentWork/AmelieLEtoileGoga/All Prey Choice - ARC  - Sheet2.csv", header=T)

# Calculating concentrations of cells (in cells per mL), normalizing to the aspirated volume and adjusting for dilution
dat$M.pmL <- dat$TotalMeso/dat$Aspirated.vol*dat$Dilu
dat$M.pmL.red <- dat$M.TotRed/dat$Aspirated.vol*dat$Dilu
dat$M.pmL.blue <- dat$M.TotBlue/dat$Aspirated.vol*dat$Dilu

dat$C.pmL <- dat$TotalCrypto/dat$Aspirated.vol*dat$Dilu
dat$C.pmL.red <- dat$C.TotRed/dat$Aspirated.vol*dat$Dilu
dat$C.pmL.blue <- dat$C.TotBlue/dat$Aspirated.vol*dat$Dilu

# Calculating proportion of blue-green cryptophytes
dat$C.propblue <- dat$C.pmL.blue/dat$C.pmL

# Rounding day to whole number so that we can group by timepoint in downstream calculations
dat$Day.Numeric <- round(dat$Time,digits=0)


# Manual adjustments to drop erroneous data
dat[dat$Exp.==11 & dat$Flask=='Flask8' & dat$Day=='D0',]$M.pmL <- NaN
dat[dat$Exp.==11 & dat$Flask=='Flask2' & dat$Day=='D3',]$C.pmL.blue <- NaN

# Convert cell counts to biovolume
# Note: ESD is actually the "biovolume (ESD)" column from the FlowCam, so already in units of um^3
dat$M.BVpmL <- dat$M.pmL*dat$TotalMeso.ESD
dat$Red.BVpmL <- dat$C.pmL.red*dat$C.Red.ESD
dat$Blue.BVpmL <- dat$C.pmL.blue*dat$C.Blue.ESD


# Other grazing rate data
meta.dat <- read.csv("/Users/hollyvm/GoogleSync/StudentWork/AmelieLEtoileGoga/Attack_Clearence rates - Mesodinium   - Sheet1.csv")

Extract relevant metadata

experiments <- unique(dat$Descrption)
exptnums <- unique(dat$Exp.)

Calculate growth and ingestion rates

We’ll follow Heinbokel & Frost and calculate ingestion rates based on the growth rates of prey in the absence and presence of predator.

To perform this calculation, we’ll need estimates of the growth rates for all species in all flasks.

Example calculation using one dataset

subdat <- dat[dat$Exp.==2,] # Subset the data
datewindow <- c(1:4) # Choose the window of dates over which to calculate growth rates
datewindow2 <- c(2:4) # Days 1 to 3 window for skipping "transfer shock" Mesodinium low growth rate datapoint

# Step 1: Calculate growth rates for all species in all flasks
summ.dat <- as.data.frame(unique(subdat$Flask))
colnames(summ.dat) <- 'Flask'
summ.dat$ExptNum <- subdat$Exp.[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Description <- subdat$Descrption[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Meso <- subdat$Meso[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Prey <- subdat$Prey[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Replicate <- subdat$Replicate[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Red.mu <- NaN
summ.dat$Blue.mu <- NaN
summ.dat$Meso.mu <- NaN
summ.dat$Meso.mu.D1toD3 <- NaN

for(i in 1:dim(summ.dat)[1]){
  FlaskID <- summ.dat$Flask[i]
  subdat2 <- subdat[subdat$Flask==summ.dat$Flask[i],]
  
  if(subdat2$Prey[1]=='0'){
    #plot(1,1,type='n',bty='n',xaxt='n',yaxt='n',xlab='',ylab='')
    #plot(1,1,type='n',bty='n',xaxt='n',yaxt='n',xlab='',ylab='')
  } else{
  # Red growth curve
  #plot(subdat2$Time,subdat2$C.pmL.red/1000,col=TAcol,pch=21,bg=TAcol.bg,log='y',xlab='Time (days)',ylab=expression(paste('Red cryptophyte (',10^3,' cells m',L^-1,')')),las=1)
  lmTA <- lm(log(subdat2$C.pmL.red)[datewindow]~subdat2$Time[datewindow])
  summ.dat$Red.mu[i] <- summary(lmTA)$coefficients[2,1]
  #xcoords <- seq(from = min(subdat$Time,na.rm = T), to = max(subdat$Time,na.rm = T), length.out=50)
  #ycoords <- exp(summary(lmTA)$coefficients[1,1])*exp(summ.dat$Red.mu[i]*xcoords)
  #lines(xcoords,ycoords/1000)
  
  # HP growth curve
  #plot(subdat2$Time,subdat2$C.pmL.blue/1000,col=HPcol,pch=21,bg=HPcol.bg,log='y',xlab='Time (days)',ylab=expression(paste('Blue cryptophyte (',10^3,' cells m',L^-1,')')),las=1)
  lmHP <- lm(log(subdat2$C.pmL.blue)[datewindow]~subdat2$Time[datewindow])
  summ.dat$Blue.mu[i] <- summary(lmHP)$coefficients[2,1]
  #ycoords <- exp(summary(lmHP)$coefficients[1,1])*exp(summ.dat$Blue.mu[i]*xcoords)
  #lines(xcoords,ycoords/1000)
  }
  # MR growth curve
  if(subdat2$Meso[1]=='0'){
    #plot(1,1,type='n',bty='n',xaxt='n',yaxt='n',xlab='',ylab='')
  } else{
  #plot(subdat2$Time,subdat2$M.pmL/1000,col=Bothcol,pch=21,bg=Bothcol.bg,log='y',xlab='Time (days)',ylab=expression(paste(italic('Mesodinium'),' (',10^3,' cells m',L^-1,')')),las=1)
    if(dim(subdat)[1] > 1){
  lmMR <- lm(log(subdat2$M.pmL)[datewindow]~subdat2$Time[datewindow])
  summ.dat$Meso.mu[i] <- summary(lmMR)$coefficients[2,1]
  #ycoords <- exp(summary(lmMR)$coefficients[1,1])*exp(summ.dat$Meso.mu[i]*xcoords)
  #lines(xcoords,ycoords/1000)
  
  lmMR <- lm(log(subdat2$M.pmL)[datewindow2] ~ subdat2$Time[datewindow2])
  summ.dat$Meso.mu.D1toD3[i] <- summary(lmMR)$coefficients[2,1]
  
  }
  }
}


# Step 2: Match growth rates by replicate
summ.dat.ctrl <- summ.dat[summ.dat$Meso=='0',]
summ.dat$ctrl.Red.mu <- summ.dat.ctrl$Red.mu[match(summ.dat$Replicate,summ.dat.ctrl$Replicate)]
summ.dat$ctrl.Blue.mu <- summ.dat.ctrl$Blue.mu[match(summ.dat$Replicate,summ.dat.ctrl$Replicate)]


# Step 3: Calculate grazing rate as the difference between growth rate in the absence of the predator and the growth rate in the presence of the predator. It has units of per day.
summ.dat$g.Red <- summ.dat$ctrl.Red.mu-summ.dat$Red.mu
summ.dat$g.Blue <- summ.dat$ctrl.Blue.mu-summ.dat$Blue.mu

# Step 4: Calculate mean population sizes
# First match initial population sizes
summ.dat$Red.d0 <- subdat[subdat$Day=='D0',]$C.pmL.red
summ.dat$Blue.d0 <- subdat[subdat$Day=='D0',]$C.pmL.blue
summ.dat$Meso.d0 <- subdat[subdat$Day=='D0',]$M.pmL
# Next assume exponential growth and solve for the mean population size:
numdays <- 3
summ.dat$Red.pop.72 <- summ.dat$Red.d0*(exp(summ.dat$Red.mu*numdays)-1) / (numdays * summ.dat$Red.mu)
summ.dat$Blue.pop.72 <- summ.dat$Blue.d0*(exp(summ.dat$Blue.mu*numdays)-1) / (numdays * summ.dat$Blue.mu)
summ.dat$Meso.pop.72 <- summ.dat$Meso.d0*(exp(summ.dat$Meso.mu*numdays)-1) / (numdays * summ.dat$Meso.mu)

# Step 5: Calculate the ingestion rate: the number of cryptophytes consumed per Mesodinium per day 
summ.dat$i.Red <- summ.dat$g.Red*summ.dat$Red.pop.72/summ.dat$Meso.pop.72
summ.dat$i.Blue <- summ.dat$g.Blue*summ.dat$Blue.pop.72/summ.dat$Meso.pop.72

# Step 6: Calculate the attack rate: the volume of water (in uL) filtered per each Mesodinium cell per day
summ.dat$a.Red <- summ.dat$g.Red/summ.dat$Meso.pop.72*1000
summ.dat$a.Blue <- summ.dat$g.Blue/summ.dat$Meso.pop.72*1000

t.test(summ.dat[summ.dat$Meso!='0'&summ.dat$Prey!='0',]$a.Red,summ.dat[summ.dat$Meso!='0'&summ.dat$Prey!='0',]$a.Blue)

## 
##  Welch Two Sample t-test
## 
## data:  summ.dat[summ.dat$Meso != "0" & summ.dat$Prey != "0", ]$a.Red and summ.dat[summ.dat$Meso != "0" & summ.dat$Prey != "0", ]$a.Blue
## t = 0.32311, df = 2.4722, p-value = 0.772
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1061739  0.1270889
## sample estimates:
## mean of x mean of y 
## 0.2641757 0.2537182

Iterate over all datasets

datewindow <- c(1:4) # Choose the window of dates over which to calculate growth rates
datewindow2 <- c(2:4) # Days 1 to 3 window for skipping "transfer shock" Mesodinium low growth rate datapoint


for(exptctr in 1:length(exptnums)){


subdat <- dat[dat$Exp.==exptnums[exptctr],] # Subset the data

# Step 1: Calculate growth rates for all species in all flasks
summ.dat <- as.data.frame(unique(subdat$Flask))
colnames(summ.dat) <- 'Flask'
summ.dat$ExptNum <- subdat$Exp.[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Description <- subdat$Descrption[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Meso <- subdat$Meso[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Prey <- subdat$Prey[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Replicate <- subdat$Replicate[match(summ.dat$Flask,subdat$Flask)]
summ.dat$Red.mu <- NaN
summ.dat$Blue.mu <- NaN
summ.dat$Meso.mu <- NaN
summ.dat$Meso.mu.D1toD3 <- NaN
summ.dat$Red.mu.BV <- NaN
summ.dat$Blue.mu.BV <- NaN
summ.dat$Meso.mu.BV <- NaN
summ.dat$Meso.mu.BV.D1toD3 <- NaN

for(i in 1:dim(summ.dat)[1]){
  FlaskID <- summ.dat$Flask[i]
  subdat2 <- subdat[subdat$Flask==summ.dat$Flask[i],]
  
  if(subdat2$Prey[1]=='0'){
  } else{
  # Red cryptophyte growth rates
  lmTA <- lm(log(subdat2$C.pmL.red)[datewindow]~subdat2$Time[datewindow])
  summ.dat$Red.mu[i] <- summary(lmTA)$coefficients[2,1]
  lmTA <- lm(log(subdat2$Red.BVpmL)[datewindow]~subdat2$Time[datewindow])
  summ.dat$Red.mu.BV[i] <- summary(lmTA)$coefficients[2,1]
  
  # Blue cryptophte growth rates
  lmHP <- lm(log(subdat2$C.pmL.blue)[datewindow]~subdat2$Time[datewindow])
  summ.dat$Blue.mu[i] <- summary(lmHP)$coefficients[2,1]
  lmHP <- lm(log(subdat2$Blue.BVpmL)[datewindow]~subdat2$Time[datewindow])
  summ.dat$Blue.mu.BV[i] <- summary(lmHP)$coefficients[2,1]
  }
  
  # MR growth rates
  if(subdat2$Meso[1]=='0'){
  } else{
    if(dim(subdat2)[1] > 1){
    # Full date window
    lmMR <- lm(log(subdat2$M.pmL)[datewindow]~subdat2$Time[datewindow])
    summ.dat$Meso.mu[i] <- summary(lmMR)$coefficients[2,1]
    lmMR <- lm(log(subdat2$M.BVpmL)[datewindow]~subdat2$Time[datewindow])
    summ.dat$Meso.mu.BV[i] <- summary(lmMR)$coefficients[2,1]
  
    # Sub date window
    lmMR <- lm(log(subdat2$M.pmL)[datewindow2] ~ subdat2$Time[datewindow2])
    summ.dat$Meso.mu.D1toD3[i] <- summary(lmMR)$coefficients[2,1]
    lmMR <- lm(log(subdat2$M.BVpmL)[datewindow2] ~ subdat2$Time[datewindow2])
    summ.dat$Meso.mu.BV.D1toD3[i] <- summary(lmMR)$coefficients[2,1]
  
  }
  }
}


# Step 2: Match growth rates by replicate
summ.dat.ctrl <- summ.dat[summ.dat$Meso=='0',]
summ.dat$ctrl.Red.mu <- summ.dat.ctrl$Red.mu[match(summ.dat$Replicate,summ.dat.ctrl$Replicate)]
summ.dat$ctrl.Blue.mu <- summ.dat.ctrl$Blue.mu[match(summ.dat$Replicate,summ.dat.ctrl$Replicate)]

# Step 3: Calculate grazing rate as the difference between growth rate in the absence of the predator and the growth rate in the presence of the predator. It has units of per day.
summ.dat$g.Red <- summ.dat$ctrl.Red.mu-summ.dat$Red.mu
summ.dat$g.Blue <- summ.dat$ctrl.Blue.mu-summ.dat$Blue.mu

# Step 4: Calculate mean population sizes
# First match initial population sizes
day0data <- subdat[subdat$Day=='D0',]
summ.dat$Red.d0 <- day0data$C.pmL.red[match(summ.dat$Flask,day0data$Flask)]
summ.dat$Blue.d0 <- day0data$C.pmL.blue[match(summ.dat$Flask,day0data$Flask)]
summ.dat$Meso.d0 <- day0data$M.pmL[match(summ.dat$Flask,day0data$Flask)]

# Next assume exponential growth and solve for the mean population size:
numdays <- 3
summ.dat$Red.pop.72 <- summ.dat$Red.d0*(exp(summ.dat$Red.mu*numdays)-1) / (numdays * summ.dat$Red.mu)
summ.dat$Blue.pop.72 <- summ.dat$Blue.d0*(exp(summ.dat$Blue.mu*numdays)-1) / (numdays * summ.dat$Blue.mu)
summ.dat$Meso.pop.72 <- summ.dat$Meso.d0*(exp(summ.dat$Meso.mu*numdays)-1) / (numdays * summ.dat$Meso.mu)

# Step 5: Calculate the ingestion rate: the number of cryptophytes consumed per Mesodinium per day 
summ.dat$i.Red <- summ.dat$g.Red*summ.dat$Red.pop.72/summ.dat$Meso.pop.72
summ.dat$i.Blue <- summ.dat$g.Blue*summ.dat$Blue.pop.72/summ.dat$Meso.pop.72

# Step 6: Calculate the attack rate: the volume of water (in uL) filtered per each Mesodinium cell per day
summ.dat$a.Red <- summ.dat$g.Red/summ.dat$Meso.pop.72*1000
summ.dat$a.Blue <- summ.dat$g.Blue/summ.dat$Meso.pop.72*1000


## REPEAT ALL CALCULATIONS WITH BIOVOLUME
# Step 2: Match growth rates by replicate; units are in per day
summ.dat.ctrl <- summ.dat[summ.dat$Meso=='0',]
summ.dat$ctrl.Red.mu.BV <- summ.dat.ctrl$Red.mu.BV[match(summ.dat$Replicate,summ.dat.ctrl$Replicate)]
summ.dat$ctrl.Blue.mu.BV <- summ.dat.ctrl$Blue.mu.BV[match(summ.dat$Replicate,summ.dat.ctrl$Replicate)]

# Step 3: Calculate grazing rate as the difference between growth rate in the absence of the predator and the growth rate in the presence of the predator. It has units of per day.
summ.dat$g.Red.BV <- summ.dat$ctrl.Red.mu.BV-summ.dat$Red.mu.BV
summ.dat$g.Blue.BV <- summ.dat$ctrl.Blue.mu.BV-summ.dat$Blue.mu.BV

# Step 4: Calculate mean population sizes
# First match initial population sizes; units of um3/mL
day0data <- subdat[subdat$Day=='D0',]
summ.dat$Red.d0.BV <- day0data$Red.BVpmL[match(summ.dat$Flask,day0data$Flask)]
summ.dat$Blue.d0.BV <- day0data$Blue.BVpmL[match(summ.dat$Flask,day0data$Flask)]
summ.dat$Meso.d0.BV <- day0data$M.BVpmL[match(summ.dat$Flask,day0data$Flask)]

# Next assume exponential growth and solve for the mean population size:; units of um3/mL
numdays <- 3
summ.dat$Red.pop.72.BV <- summ.dat$Red.d0.BV*(exp(summ.dat$Red.mu.BV*numdays)-1) / (numdays * summ.dat$Red.mu.BV)
summ.dat$Blue.pop.72.BV <- summ.dat$Blue.d0.BV*(exp(summ.dat$Blue.mu.BV*numdays)-1) / (numdays * summ.dat$Blue.mu.BV)
summ.dat$Meso.pop.72.BV <- summ.dat$Meso.d0.BV*(exp(summ.dat$Meso.mu.BV*numdays)-1) / (numdays * summ.dat$Meso.mu.BV)

# Step 5: Calculate the ingestion rate: the number of cryptophytes consumed per Mesodinium per day 
# units of per day * um3 prey /ml  / um3 pred /mL = um3 prey / um3 pred per day
summ.dat$i.Red.BV <- summ.dat$g.Red.BV*summ.dat$Red.pop.72.BV/summ.dat$Meso.pop.72.BV
summ.dat$i.Blue.BV <- summ.dat$g.Blue.BV*summ.dat$Blue.pop.72.BV/summ.dat$Meso.pop.72.BV

# Step 6: Calculate the attack rate: 
# units of per day / um3 pred per mL = mL per um3 pred per day
# 1 mL = 10^12 um^3, so conversion by multiplying by 10^12 gives equivalent volumes, e.g. mL per mL Meso biovolume per day
# Dividing by 1000 puts into units of L filtered per mL Meso biovolume (or mL filtered per uL of Meso biovolume) and makes the plots nicer
summ.dat$a.Red.BV <- summ.dat$g.Red.BV/summ.dat$Meso.pop.72.BV*(10^12)/1000
summ.dat$a.Blue.BV <- summ.dat$g.Blue/summ.dat$Meso.pop.72.BV*(10^12)/1000


# Save the summary data to a mega-table
if(exptctr==1){
  summ.dat.concat <- summ.dat 
} else{
  summ.dat.concat <- rbind(summ.dat.concat,summ.dat)
}


}

# Add some additional metadata columns to allow us to subset the data
summ.dat.concat$Description <- dat$Description[match(summ.dat.concat$ExptNum,dat$Exp.)]
summ.dat.concat$Meso.name <- substr(summ.dat.concat$Description,1,2)
summ.dat.concat$Red.name <- substr(summ.dat.concat$Description,4,5)
summ.dat.concat$Blue.name <- substr(summ.dat.concat$Description,7,8)
summ.dat.concat$Meso.2char <- substr(summ.dat.concat$Meso,1,2)

Ingestion rate results

Raw ingestion rates

# Setting figure panel margins
par(mar=c(1.5,2,1,1))

# Setting common y-axis limits
ylims <- matrix(c(0,10,0,10),nrow=2,ncol=2,byrow=T)

# Setting y-axis labels
ylabels <- c(expression(paste('Ingestion rate (Prey · ',italic('M. rubrum')^-1,' · ',d^-1,')')),expression(paste('Ingestion rate (Prey · ',italic('M. chamaeleon')^-1,' · ',d^-1,')')))
ylabelcenter <- c(-.15,-.1)


# Using a fancy layout command to make the figure a little prettier
colsperpanel <- 3
rowsperpanel <- 3
layoutmatrix <- matrix(data=NA, nrow=2*rowsperpanel+1, ncol = 3*colsperpanel+1)
for(j in 1:length(Blue.cryptos)){
  for(k in 1:length(Red.cryptos)){
    layoutmatrix[((j-1)*rowsperpanel + 1):(j*rowsperpanel), ((k-1)*colsperpanel+2):(k*colsperpanel+1)] <- (j-1)*3+k
  }
}
layoutmatrix[,1] <- 7
layoutmatrix[dim(layoutmatrix)[1],] <- 8

layout(layoutmatrix)


for(i in 1:length(Mesos)){ # For each Mesodinium, make a new figure
  for(j in 1:length(Blue.cryptos)){ # For each blue cryptophyte, make a new row
    for(k in 1:length(Red.cryptos)){ # For each red cryptophyte, make a new panel
      # Subset the data
      subdat <- summ.dat.concat[summ.dat.concat$Meso.name==Mesos[i] & summ.dat.concat$Blue.name==Blue.cryptos[j] & summ.dat.concat$Red.name==Red.cryptos[k],]
      # Make the relevant barplot
      if(dim(subdat)[1] > 0){
        means <- c(mean(subdat$i.Blue,na.rm=T),mean(subdat$i.Red,na.rm=T))
        ses <- c(se(subdat$i.Blue,na.rm=T),se(subdat$i.Red,na.rm = T))
        barplot(means,las=1,names=c(Blue.names[j],Red.names[k]),space=0.05,ylim=c(0,1.1*max(means+ses)),col=c('white',TAcol.bg))
        barplot(means,las=1,space=0.05,ylim=c(0,1.05*max(means+ses)),col=c(HPcol,TAcol),density=c(dens.list[j],dens.list[k]),add=T,angle=c(angle.list[j],angle.list[k]))
        arrows(c(.55,1.6),means,c(.55,1.6),means+ses,code=2,angle=90,length=.1)
        # Add panel label
        text(-.1,max(means+ses)*1.06,paste(panel.labs[j,k],Blue.cryptos[j],'+',Red.cryptos[k]),pos=4)
        # Perform statistical test
        ttest <- t.test(subdat[subdat$Meso!='0',]$i.Red,subdat[subdat$Meso!='0',]$i.Blue) 
        if(ttest$p.value < 0.001){
          text(1.075, 1.05*max(means,na.rm=T), '***',cex=1.5)
        } else{
          if(ttest$p.value < 0.01){
            text(1.075, 1.05*max(means,na.rm=T),'**',cex=1.5)
          } else{
            if(ttest$p.value < 0.05){
              text(1.075, 1.05*max(means,na.rm=T),'*',cex=1.5)
            } else{
              if(ttest$p.value < 0.056){
                text(1.075,1.05*max(means,na.rm=T),'.',cex=1.5)
              }
            }
          }
        }
#text(1.3,0.285,'**',cex=1.5)
      } else{
        # If there are no data, create a blank plot as a placeholder
        plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty='n')
      }
      if(j==2){ if(k == 2){mtext('Cryptophyte prey species',side=1,line=2.5,cex=.7)}}
      if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.5,cex=.7, at = ylabelcenter[i])}}
    }
    
  }
  # Blank plots as placeholders
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='',bty='n')
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty = 'n')
}

Common y-axis

# Setting figure panel margins
par(mar=c(1.5,2,1,1))

# Setting common y-axis limits
ylims <- matrix(c(0,1.6,0,11.5),nrow=2,ncol=2,byrow=T)

# Setting y-axis labels
ylabels <- c(expression(paste('Ingestion rate (Prey · ',italic('M. rubrum')^-1,' · ',d^-1,')')),expression(paste('Ingestion rate (Prey · ',italic('M. chamaeleon')^-1,' · ',d^-1,')')))
ylabelcenter <- c(-.3,-1.5)

# Using a fancy layout command to make the figure a little prettier
colsperpanel <- 3
rowsperpanel <- 3
layoutmatrix <- matrix(data=NA, nrow=2*rowsperpanel+1, ncol = 3*colsperpanel+1)
for(j in 1:length(Blue.cryptos)){
  for(k in 1:length(Red.cryptos)){
    layoutmatrix[((j-1)*rowsperpanel + 1):(j*rowsperpanel), ((k-1)*colsperpanel+2):(k*colsperpanel+1)] <- (j-1)*3+k
  }
}
layoutmatrix[,1] <- 7
layoutmatrix[dim(layoutmatrix)[1],] <- 8

layout(layoutmatrix)


for(i in 1:length(Mesos)){ # For each Mesodinium, make a new figure
  for(j in 1:length(Blue.cryptos)){ # For each blue cryptophyte, make a new row
    for(k in 1:length(Red.cryptos)){ # For each red cryptophyte, make a new panel
      # Subset the data
      subdat <- summ.dat.concat[summ.dat.concat$Meso.name==Mesos[i] & summ.dat.concat$Blue.name==Blue.cryptos[j] & summ.dat.concat$Red.name==Red.cryptos[k],]
      # Make the relevant barplot
      if(dim(subdat)[1] > 0){
        means <- c(mean(subdat$i.Blue,na.rm=T),mean(subdat$i.Red,na.rm=T))
        ses <- c(se(subdat$i.Blue,na.rm=T),se(subdat$i.Red,na.rm = T))
        barplot(means,las=1,names=c(Blue.names[j],Red.names[k]),space=0.05,ylim=ylims[i,],col=c('white',TAcol.bg))
        barplot(means,las=1,space=0.05,col=c(HPcol,TAcol),density=c(dens.list[j],dens.list[k]),add=T,angle=c(angle.list[j],angle.list[k]))
        arrows(c(.55,1.6),means,c(.55,1.6),means+ses,code=2,angle=90,length=.1)
        # Add panel label
        text(-.1,ylims[i,2]*.9,paste(panel.labs[j,k],Blue.cryptos[j],'+',Red.cryptos[k]),pos=4)
        # Perform statistical test
        ttest <- t.test(subdat[subdat$Meso!='0',]$i.Red,subdat[subdat$Meso!='0',]$i.Blue) 
        if(ttest$p.value < 0.001){
          text(1.075, 1.1*max(means,na.rm=T), '***',cex=1.5)
        } else{
          if(ttest$p.value < 0.01){
            text(1.075, 1.1*max(means,na.rm=T),'**',cex=1.5)
          } else{
            if(ttest$p.value < 0.05){
              text(1.075, 1.1*max(means,na.rm=T),'*',cex=1.5)
            } else{
              if(ttest$p.value < 0.056){
                text(1.075,1.1*max(means,na.rm=T),'.',cex=1.5)
              }
            }
          }
        }
#text(1.3,0.285,'**',cex=1.5)
      } else{
        # If there are no data, create a blank plot as a placeholder
        plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty='n')
      }
      if(j==2){ if(k == 2){mtext('Cryptophyte prey species',side=1,line=2.5,cex=.7)}}
      if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.5,cex=.7, at = ylabelcenter[i])}}
    }
    
  }
  # Blank plots as placeholders
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='',bty='n')
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty = 'n')
}

Attack rate results

# Setting figure panel margins
par(mar=c(1.5,2,1,1))

# Setting common y-axis limits
ylims <- matrix(c(0,.38,0,1.7),nrow=2,ncol=2,byrow=T)

# Setting y-axis labels
ylabels <- c(expression(paste('Clearance rate (μL · ',italic('M. rubrum')^-1,' · ',d^-1,')')),expression(paste('Clearance rate (μL · ',italic('M. chamaeleon')^-1,' · ',d^-1,')')))
ylabelcenter <- c(-.02,-.2)

# Using a fancy layout command to make the figure a little prettier
colsperpanel <- 3
rowsperpanel <- 3
layoutmatrix <- matrix(data=NA, nrow=2*rowsperpanel+1, ncol = 3*colsperpanel+1)
for(j in 1:length(Blue.cryptos)){
  for(k in 1:length(Red.cryptos)){
    layoutmatrix[((j-1)*rowsperpanel + 1):(j*rowsperpanel), ((k-1)*colsperpanel+2):(k*colsperpanel+1)] <- (j-1)*3+k
  }
}
layoutmatrix[,1] <- 7
layoutmatrix[dim(layoutmatrix)[1],] <- 8

layout(layoutmatrix)


for(i in 1:length(Mesos)){ # For each Mesodinium, make a new figure
  for(j in 1:length(Blue.cryptos)){ # For each blue cryptophyte, make a new row
    for(k in 1:length(Red.cryptos)){ # For each red cryptophyte, make a new panel
      # Subset the data
      subdat <- summ.dat.concat[summ.dat.concat$Meso.name==Mesos[i] & summ.dat.concat$Blue.name==Blue.cryptos[j] & summ.dat.concat$Red.name==Red.cryptos[k],]
      # Make the relevant barplot
      if(dim(subdat)[1] > 0){
        means <- c(mean(subdat$a.Blue,na.rm=T),mean(subdat$a.Red,na.rm=T))
        ses <- c(se(subdat$a.Blue,na.rm=T),se(subdat$a.Red,na.rm = T))
        barplot(means,las=1,names=c(Blue.names[j],Red.names[k]),space=0.05,ylim=c(0,1.15*max(means+ses)),col=c('white',TAcol.bg))
        barplot(means,las=1,space=0.05,col=c(HPcol,TAcol),density=c(dens.list[j],dens.list[k]),add=T,angle=c(angle.list[j],angle.list[k]))
        arrows(c(.55,1.6),means,c(.55,1.6),means+ses,code=2,angle=90,length=.1)
        # Add panel label
        text(-.1,max(means+ses,na.rm=T)*1.07,paste(panel.labs[j,k],Blue.cryptos[j],'+',Red.cryptos[k]),pos=4)
        # Conduct statistical test
        ttest <- t.test(subdat[subdat$Meso!='0',]$a.Red,subdat[subdat$Meso!='0',]$a.Blue) 
        if(ttest$p.value < 0.001){
          text(1.075, 1.05*max(means,na.rm=T), '***',cex=1.5)
        } else{
          if(ttest$p.value < 0.01){
            text(1.075, 1.05*max(means,na.rm=T),'**',cex=1.5)
          } else{
            if(ttest$p.value < 0.05){
              text(1.075, 1.05*max(means,na.rm=T),'*',cex=1.5)
            } else{
              if(ttest$p.value < 0.056){
                text(1.075, 1.05*max(means,na.rm=T),'.',cex=1.5)
              }
            }
          }
        }
#text(1.3,0.285,'**',cex=1.5)
      } else{
        # If there are no data, create a blank plot as a placeholder
        plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty='n')
      }
      if(j==2){ if(k == 2){mtext('Cryptophyte prey species',side=1,line=2.5,cex=.7)}}
      if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.5,cex=.7, at = ylabelcenter[i])}}
    }
    
  }
  # Blank plots as placeholders
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='',bty='n')
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty = 'n')
}

Common y-axis

# Setting figure panel margins
par(mar=c(1.5,2,1,1))

# Setting common y-axis limits
ylims <- matrix(c(0,.38,0,1.8),nrow=2,ncol=2,byrow=T)

# Setting y-axis labels
ylabels <- c(expression(paste('Clearance rate (μL · ',italic('M. rubrum')^-1,' · ',d^-1,')')),expression(paste('Clearance rate (μL · ',italic('M. chamaeleon')^-1,' · ',d^-1,')')))
ylabelcenter <- c(-.02,-.2)

# Using a fancy layout command to make the figure a little prettier
colsperpanel <- 3
rowsperpanel <- 3
layoutmatrix <- matrix(data=NA, nrow=2*rowsperpanel+1, ncol = 3*colsperpanel+1)
for(j in 1:length(Blue.cryptos)){
  for(k in 1:length(Red.cryptos)){
    layoutmatrix[((j-1)*rowsperpanel + 1):(j*rowsperpanel), ((k-1)*colsperpanel+2):(k*colsperpanel+1)] <- (j-1)*3+k
  }
}
layoutmatrix[,1] <- 7
layoutmatrix[dim(layoutmatrix)[1],] <- 8

layout(layoutmatrix)


for(i in 1:length(Mesos)){ # For each Mesodinium, make a new figure
  for(j in 1:length(Blue.cryptos)){ # For each blue cryptophyte, make a new row
    for(k in 1:length(Red.cryptos)){ # For each red cryptophyte, make a new panel
      # Subset the data
      subdat <- summ.dat.concat[summ.dat.concat$Meso.name==Mesos[i] & summ.dat.concat$Blue.name==Blue.cryptos[j] & summ.dat.concat$Red.name==Red.cryptos[k],]
      # Make the relevant barplot
      if(dim(subdat)[1] > 0){
        means <- c(mean(subdat$a.Blue,na.rm=T),mean(subdat$a.Red,na.rm=T))
        ses <- c(se(subdat$a.Blue,na.rm=T),se(subdat$a.Red,na.rm = T))
        barplot(means,las=1,names=c(Blue.names[j],Red.names[k]),space=0.05,ylim=ylims[i,],col=c('white',TAcol.bg))
        barplot(means,las=1,space=0.05,col=c(HPcol,TAcol),density=c(dens.list[j],dens.list[k]),add=T,angle=c(angle.list[j],angle.list[k]))
        arrows(c(.55,1.6),means,c(.55,1.6),means+ses,code=2,angle=90,length=.1)
        ttest <- t.test(subdat[subdat$Meso!='0',]$a.Red,subdat[subdat$Meso!='0',]$a.Blue) 
        
        # Add panel label
        text(-.1,ylims[i,2]*.96,paste(panel.labs[j,k],Blue.cryptos[j],'+',Red.cryptos[k]),pos=4)
        if(ttest$p.value < 0.001){
          text(1.075, 0.05*ylims[i,2]+max(means,na.rm=T), '***',cex=1.5)
        } else{
          if(ttest$p.value < 0.01){
            text(1.075, 0.05*ylims[i,2]+max(means,na.rm=T),'**',cex=1.5)
          } else{
            if(ttest$p.value < 0.05){
              text(1.075, 0.05*ylims[i,2]+max(means,na.rm=T),'*',cex=1.5)
            } else{
              if(ttest$p.value < 0.056){
                text(1.075, 0.05*ylims[i,2]+max(means,na.rm=T),'.',cex=1.5)
              }
            }
          }
        }
#text(1.3,0.285,'**',cex=1.5)
      } else{
        # If there are no data, create a blank plot as a placeholder
        plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty='n')
      }
      if(j==2){ if(k == 2){mtext('Cryptophyte prey species',side=1,line=2.5,cex=.7)}}
      if(i==2){if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.3,cex=.7, at = ylabelcenter[i])}}}
      if(i==1){if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.8,cex=.7, at = ylabelcenter[i])}}}
    }
    
  }
  # Blank plots as placeholders
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='',bty='n')
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty = 'n')
}

Ingestion results with biovolume

# Setting figure panel margins
par(mar=c(1.5,2,1,1))

# Setting common y-axis limits
ylims <- matrix(c(0,35,0,265),nrow=2,ncol=2,byrow=T)

# Setting y-axis labels
ylabels <- c(expression(paste('Clearance rate (mL · μL ',italic('M. rubrum'),' ',biovolume^-1,' · ',d^-1,')')),expression(paste('Clearance rate (mL · μL ',italic('M. chamaeleon'),' ',biovolume^-1,' · ',d^-1,')')))
#ylabels <- c('MR attack rate, biovolume','MC attack rate, biovolume')
ylabelcenter <- c(-4,-40)

# Using a fancy layout command to make the figure a little prettier
colsperpanel <- 3
rowsperpanel <- 3
layoutmatrix <- matrix(data=NA, nrow=2*rowsperpanel+1, ncol = 3*colsperpanel+1)
for(j in 1:length(Blue.cryptos)){
  for(k in 1:length(Red.cryptos)){
    layoutmatrix[((j-1)*rowsperpanel + 1):(j*rowsperpanel), ((k-1)*colsperpanel+2):(k*colsperpanel+1)] <- (j-1)*3+k
  }
}
layoutmatrix[,1] <- 7
layoutmatrix[dim(layoutmatrix)[1],] <- 8

layout(layoutmatrix)


for(i in 1:length(Mesos)){ # For each Mesodinium, make a new figure
  for(j in 1:length(Blue.cryptos)){ # For each blue cryptophyte, make a new row
    for(k in 1:length(Red.cryptos)){ # For each red cryptophyte, make a new panel
      # Subset the data
      subdat <- summ.dat.concat[summ.dat.concat$Meso.name==Mesos[i] & summ.dat.concat$Blue.name==Blue.cryptos[j] & summ.dat.concat$Red.name==Red.cryptos[k],]
      # Make the relevant barplot
      if(dim(subdat)[1] > 0){
        means <- c(mean(subdat$a.Blue.BV,na.rm=T),mean(subdat$a.Red.BV,na.rm=T))
        ses <- c(se(subdat$a.Blue.BV,na.rm=T),se(subdat$a.Red.BV,na.rm = T))
        barplot(means,las=1,names=c(Blue.names[j],Red.names[k]),space=0.05,ylim=ylims[i,],col=c('white',TAcol.bg))
        barplot(means,las=1,space=0.05,ylim=ylims[i,],col=c(HPcol,TAcol),density=c(dens.list[j],dens.list[k]),add=T,angle=c(angle.list[j],angle.list[k]))
        arrows(c(.55,1.6),means,c(.55,1.6),means+ses,code=2,angle=90,length=.1)
        ttest <- t.test(subdat[subdat$Meso!='0',]$a.Red.BV,subdat[subdat$Meso!='0',]$a.Blue.BV) 
        
        # Add panel label
        text(-.1,ylims[i,2]*.96,paste(panel.labs[j,k],Blue.cryptos[j],'+',Red.cryptos[k]),pos=4)
        
        if(ttest$p.value < 0.001){
          text(1.075, 1.05*max(means,na.rm=T), '***',cex=1.5)
        } else{
          if(ttest$p.value < 0.01){
            text(1.075,1.05* max(means,na.rm=T),'**',cex=1.5)
          } else{
            if(ttest$p.value < 0.05){
              text(1.075, 1.05*max(means,na.rm=T),'*',cex=1.5)
            }else{
              if(ttest$p.value < 0.056){
                text(1.075, 1.05*max(means,na.rm=T),'.',cex=1.5)
              }
            }
          }
        }
#text(1.3,0.285,'**',cex=1.5)
      } else{
        # If there are no data, create a blank plot as a placeholder
        plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty='n')
      }
      if(j==2){ if(k == 2){mtext('Cryptophyte prey species',side=1,line=2.5,cex=.7)}}
      if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.5,cex=.7, at = ylabelcenter[i])}}
    }
    
  }
  # Blank plots as placeholders
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='',bty='n')
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty = 'n')
}

Preference assay

#head(summ.dat.concat)

# Calculate proportion abundance
summ.dat.concat$Prop.Red.Abund <- summ.dat.concat$Red.pop.72 / (summ.dat.concat$Red.pop.72 + summ.dat.concat$Blue.pop.72)

# Calculate proportion ingested
summ.dat.concat$Prop.Red.Ingest <- summ.dat.concat$i.Red / (summ.dat.concat$i.Red + summ.dat.concat$i.Blue)

# Plot
par(mar=c(4,4,1,1),mfrow=c(1,1))
plot(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Prop.Red.Abund,summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Prop.Red.Ingest, xlim=c(0.25,1), ylim=c(0.25,1), type='n', xlab = '', ylab = '',las=1 )
mtext(expression(paste('Proportion red cryptophytes available')),side=1,line=2.5)
mtext(expression(paste('Proportion red cryptophytes ingested')), side = 2, line = 2.5)
abline(a=0,b=1,col='gray50',lty=2)
text(0.28,0.31,'1:1 line',srt=45,cex=.8,col='gray50')

lm.MR <- lm(Prop.Red.Ingest ~ Prop.Red.Abund, data = summ.dat.concat[summ.dat.concat$Meso.2char=='MR',])
abline(lm.MR,col='black')
text(0.35,0.7,expression(italic("M. rubrum")))
text(0.35,0.65,expression(paste('(',R^2,' = 0.0097)')))
arrows(0.38,0.715,0.48,0.785,length=0.1)

lm.MC <- lm(Prop.Red.Ingest ~ Prop.Red.Abund, data = summ.dat.concat[summ.dat.concat$Meso.2char=='MC',])
abline(lm.MC,col=HPcol)
text(0.68,0.45,expression(italic("M. chamaeleon")),col=HPcol)
text(0.68,0.4,expression(paste('(',R^2,' = 0.7222)')),col=HPcol)
arrows(0.55,0.465,0.49,0.53,length=0.1,col=HPcol)

points(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Prop.Red.Abund,summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Prop.Red.Ingest, pch = c(21,22,24)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Red.name)], bg = c('white','black')[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Blue.name)], cex = c(1.5,1.5,1.2)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Red.name)])

points(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Prop.Red.Abund,summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Prop.Red.Ingest, pch = c(21,22,24)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Red.name)], col = HPcol, bg = c('white',HPcol)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Blue.name)], cex = c(1.5,1.5,1.2)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Red.name)])
#abline(a=0,b=1)

legend(x = 'topleft', inset = c(0.01,0.01), legend = c('CM + RS', 'CM + SM','CM + TA', 'HP + RS','HP + SM','HP + TA'), pch=c(21,22,24,21,22,24), pt.cex=c(1.5,1.5,1.2,1.5,1.5,1.2), pt.bg = c('white','white','white','black','black','black'),cex=.8)

Growth rate results

# Compute the max growth rate
summ.dat.concat$Meso.max.mu <- NaN
for(i in 1:dim(summ.dat.concat)[1]){
  if(summ.dat.concat$Meso[i]!='0'){
  summ.dat.concat$Meso.max.mu[i] <- max(c(summ.dat.concat$Meso.mu[i],summ.dat.concat$Meso.mu.D1toD3[i]),na.rm = T)
  }}

# Get rid of any -Inf datapoints (which arise if both growth rates are equal to NaN)
for(i in 1:dim(summ.dat.concat)[1]){
  if(!is.na(summ.dat.concat$Meso.max.mu[i])){
    if(summ.dat.concat$Meso.max.mu[i]==-Inf){
      summ.dat.concat$Meso.max.mu[i]<-NaN
    }
  }}

Mesodinium rubrum growth rates

Tukey tests show no significant differences among treatments, which makes sense if (1) we assume that the M. rubrum were already relatively well-fed on T. amphi plastids and growing at pretty decent rates, and (2) it takes longer than 72hrs to see a change in growth rate in M. rubrum.

par(mar=c(3,4.5,2,1),mfrow=c(2,1))
summ.dat.concat$Prey2 <- paste(summ.dat.concat$Blue.name,summ.dat.concat$Red.name,sep='+')
for(i in 1:dim(summ.dat.concat)[1]){
  if(summ.dat.concat$Prey[i]=='0'){summ.dat.concat$Prey2[i] <- '0'}
}
summ.dat.concat[summ.dat.concat$Prey2==0,]$Prey2 <- ' Starved '

bargraph.CI(Prey2,Meso.mu, data=summ.dat.concat[summ.dat.concat$Meso=='MRTA',], las = 1, xlab='', ylab = expression(paste(italic('M. rubrum'),' growth rate (', d^{-1},')',sep='')), col = c('white',rep('gray60',6)), ylim =c(0,0.368),lc=F)
#TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat[summ.dat.concat$Meso=='MRTA',]))
text(x=seq(from=0.7,to=7.9,length.out=7),y=c(0.26,0.3,0.36,0.345,0.315,0.275,0.265),rep('a',7))
text(0.1,.355,panel.labs[1,1],cex=1.2)

par(mar=c(4,4.5,1,1))
bargraph.CI(Prey2,Meso.mu.BV, data=summ.dat.concat[summ.dat.concat$Meso=='MRTA',], las = 1, xlab='Prey combination', ylab = expression(paste(italic('M. rubrum'),' biovolume growth rate (', d^{-1},')',sep='')), col = c('white',rep('gray60',6)),ylim=c(0,.44),lc=F)
#TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat[summ.dat.concat$Meso=='MRTA',]))
text(x=seq(from=0.7,to=7.9,length.out=7),y=c(0.23,0.21,0.428,0.355,0.325,0.29,0.3),c('a','a','b','ab','ab','ab','ab'))
text(0.1,.42,panel.labs[1,2],cex=1.2)

TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat[summ.dat.concat$Meso=='MRTA',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.mu ~ Prey2, data = summ.dat.concat[summ.dat.concat$Meso == "MRTA", ])
## 
## $Prey2
##                          diff         lwr        upr     p adj
## CM+RS- Starved   0.0221668728 -0.10431375 0.14864749 0.9971673
## CM+SM- Starved   0.0968066820 -0.02967394 0.22328730 0.2153250
## CM+TA- Starved   0.0497977011 -0.07668292 0.17627832 0.8562483
## HP+RS- Starved   0.0018292355 -0.12465138 0.12830985 1.0000000
## HP+SM- Starved   0.0011884354 -0.12529218 0.12766905 1.0000000
## HP+TA- Starved   0.0032892827 -0.12319134 0.12976990 1.0000000
## CM+SM-CM+RS      0.0746398092 -0.08391204 0.23319165 0.7285222
## CM+TA-CM+RS      0.0276308283 -0.13092102 0.18618267 0.9972541
## HP+RS-CM+RS     -0.0203376373 -0.17888948 0.13821421 0.9995101
## HP+SM-CM+RS     -0.0209784374 -0.17953028 0.13757341 0.9994151
## HP+TA-CM+RS     -0.0188775901 -0.17742944 0.13967426 0.9996807
## CM+TA-CM+SM     -0.0470089809 -0.20556083 0.11154286 0.9575800
## HP+RS-CM+SM     -0.0949774465 -0.25352929 0.06357440 0.4778244
## HP+SM-CM+SM     -0.0956182466 -0.25417009 0.06293360 0.4701228
## HP+TA-CM+SM     -0.0935173993 -0.25206925 0.06503445 0.4955285
## HP+RS-CM+TA     -0.0479684657 -0.20652031 0.11058338 0.9533961
## HP+SM-CM+TA     -0.0486092657 -0.20716111 0.10994258 0.9504523
## HP+TA-CM+TA     -0.0465084185 -0.20506026 0.11204343 0.9596578
## HP+SM-HP+RS     -0.0006408001 -0.15919265 0.15791105 1.0000000
## HP+TA-HP+RS      0.0014600472 -0.15709180 0.16001189 1.0000000
## HP+TA-HP+SM      0.0021008473 -0.15645100 0.16065269 1.0000000

TukeyHSD(aov(Meso.mu.BV~Prey2,data=summ.dat.concat[summ.dat.concat$Meso=='MRTA',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.mu.BV ~ Prey2, data = summ.dat.concat[summ.dat.concat$Meso == "MRTA", ])
## 
## $Prey2
##                        diff         lwr        upr     p adj
## CM+RS- Starved  -0.02727155 -0.15375784 0.09921474 0.9913543
## CM+SM- Starved   0.16334172  0.03685543 0.28982801 0.0061084
## CM+TA- Starved   0.08904235 -0.03744394 0.21552863 0.2973998
## HP+RS- Starved   0.03867264 -0.08781365 0.16515892 0.9510720
## HP+SM- Starved   0.05194293 -0.07454336 0.17842921 0.8312497
## HP+TA- Starved   0.06902678 -0.05745951 0.19551307 0.5823812
## CM+SM-CM+RS      0.19061327  0.03205432 0.34917223 0.0118553
## CM+TA-CM+RS      0.11631390 -0.04224506 0.27487285 0.2555045
## HP+RS-CM+RS      0.06594419 -0.09261477 0.22450314 0.8231274
## HP+SM-CM+RS      0.07921448 -0.07934448 0.23777343 0.6736136
## HP+TA-CM+RS      0.09629833 -0.06226062 0.25485729 0.4620500
## CM+TA-CM+SM     -0.07429937 -0.23285833 0.08425958 0.7325381
## HP+RS-CM+SM     -0.12466908 -0.28322804 0.03388987 0.1916634
## HP+SM-CM+SM     -0.11139879 -0.26995775 0.04716016 0.2994856
## HP+TA-CM+SM     -0.09431494 -0.25287389 0.06424402 0.4858833
## HP+RS-CM+TA     -0.05036971 -0.20892866 0.10818924 0.9417461
## HP+SM-CM+TA     -0.03709942 -0.19565837 0.12145953 0.9867746
## HP+TA-CM+TA     -0.02001556 -0.17857452 0.13854339 0.9995531
## HP+SM-HP+RS      0.01327029 -0.14528866 0.17182924 0.9999591
## HP+TA-HP+RS      0.03035415 -0.12820481 0.18891310 0.9954152
## HP+TA-HP+SM      0.01708386 -0.14147510 0.17564281 0.9998208

Note: Seeing the biovolume growth rate increase is actually consistent w/ the MR + HP paper from 2024, in which we saw the cells get distorted and large after prolonged feeding with HP. It may be the case that ingesting “incorrect” prey causes issues with cell division.

Mesodinium chamaeleon growth rates

It’s notable that when fed TA, M. chamaeleon’s growth rate is rarely statistically different from the control.

Including all control data

par(mar=c(3,4.5,2,1),mfrow=c(2,1))
summ.dat.concat$Prey2 <- paste(summ.dat.concat$Blue.name,summ.dat.concat$Red.name,sep='+')
for(i in 1:dim(summ.dat.concat)[1]){
  if(summ.dat.concat$Prey[i]=='0'){summ.dat.concat$Prey2[i] <- '0'}
}
summ.dat.concat[summ.dat.concat$Prey2==0,]$Prey2 <- ' Starved '
summ.dat.concat$Description2 <- paste(summ.dat.concat$Description,summ.dat.concat$Prey,sep='.')

bargraph.CI(Prey2,Meso.mu, data=summ.dat.concat[summ.dat.concat$Meso.name=='MC',], las = 1, xlab='', ylab = expression(paste(italic('M. chamaeleon'),' growth rate (', d^{-1},')',sep='')), col = c('white',rep('gray60',6)), ylim =c(0,0.67),lc=F)
#TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat[summ.dat.concat$Meso.name=='MC',]))
text(x=seq(from=0.7,to=7.9,length.out=7),y=c(0.265,0.61,0.645,0.465,0.63,0.505,0.395),c('a','b','ab','ab','ab','ab','ab'))
text(0.1,0.64,panel.labs[1,1],cex=1.2)

par(mar=c(4,4.5,1,1))
bargraph.CI(Prey2,Meso.mu.BV, data=summ.dat.concat[summ.dat.concat$Meso.name=='MC',], las = 1, xlab='Prey combination', ylab = '', col = c('white',rep('gray60',6)),ylim=c(0,.759),lc=F)
mtext(expression(paste(italic('M. chamaeleon'),' biovolume growth rate (', d^{-1},')',sep='')),side=2,line=3,cex=1)
#TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat[summ.dat.concat$Meso.name=='MC',]))
text(x=seq(from=0.7,to=7.9,length.out=7),y=c(0.305,0.63,0.707,0.595,0.742,0.598,0.458),c('a','b','b','ab','b','ab','ab'))
text(0.1,.73,panel.labs[1,2],cex=1.2)

TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat[summ.dat.concat$Meso.name=='MC',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.mu ~ Prey2, data = summ.dat.concat[summ.dat.concat$Meso.name == "MC", ])
## 
## $Prey2
##                        diff          lwr       upr     p adj
## CM+RS- Starved   0.35441514  0.036633800 0.6721965 0.0214889
## CM+SM- Starved   0.31447426 -0.003307084 0.6322556 0.0538054
## CM+TA- Starved   0.21206783 -0.105713513 0.5298492 0.3685418
## HP+RS- Starved   0.29744669 -0.020334652 0.6152280 0.0778050
## HP+SM- Starved   0.24509433 -0.072687011 0.5628757 0.2161278
## HP+TA- Starved   0.11657041 -0.201210936 0.4343517 0.9003994
## CM+SM-CM+RS     -0.03994088 -0.452348412 0.3724666 0.9999165
## CM+TA-CM+RS     -0.14234731 -0.554754841 0.2700602 0.9232937
## HP+RS-CM+RS     -0.05696845 -0.469375979 0.3554391 0.9993482
## HP+SM-CM+RS     -0.10932081 -0.521728339 0.3030867 0.9778166
## HP+TA-CM+RS     -0.23784474 -0.650252264 0.1745628 0.5377987
## CM+TA-CM+SM     -0.10240643 -0.514813957 0.3100011 0.9840411
## HP+RS-CM+SM     -0.01702757 -0.429435096 0.3953800 0.9999995
## HP+SM-CM+SM     -0.06937993 -0.481787455 0.3430276 0.9980189
## HP+TA-CM+SM     -0.19790385 -0.610311380 0.2145037 0.7271772
## HP+RS-CM+TA      0.08537886 -0.327028667 0.4977864 0.9938385
## HP+SM-CM+TA      0.03302650 -0.379381026 0.4454340 0.9999727
## HP+TA-CM+TA     -0.09549742 -0.507904951 0.3169101 0.9888648
## HP+SM-HP+RS     -0.05235236 -0.464759888 0.3600552 0.9995983
## HP+TA-HP+RS     -0.18087628 -0.593283812 0.2315312 0.7996272
## HP+TA-HP+SM     -0.12852392 -0.540931453 0.2838836 0.9516206

TukeyHSD(aov(Meso.mu.BV~Prey2,data=summ.dat.concat[summ.dat.concat$Meso.name=='MC',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.mu.BV ~ Prey2, data = summ.dat.concat[summ.dat.concat$Meso.name == "MC", ])
## 
## $Prey2
##                        diff          lwr       upr     p adj
## CM+RS- Starved   0.34100747  0.013541369 0.6684736 0.0371708
## CM+SM- Starved   0.33584614  0.008380039 0.6633122 0.0416564
## CM+TA- Starved   0.30059435 -0.026871746 0.6280605 0.0877957
## HP+RS- Starved   0.36278890  0.035322799 0.6902550 0.0227240
## HP+SM- Starved   0.28668270 -0.040783399 0.6141488 0.1157256
## HP+TA- Starved   0.13037859 -0.197087506 0.4578447 0.8609341
## CM+SM-CM+RS     -0.00516133 -0.430137460 0.4198148 1.0000000
## CM+TA-CM+RS     -0.04041312 -0.465389245 0.3845630 0.9999250
## HP+RS-CM+RS      0.02178143 -0.403194701 0.4467576 0.9999981
## HP+SM-CM+RS     -0.05432477 -0.479300899 0.3706514 0.9995819
## HP+TA-CM+RS     -0.21062888 -0.635605005 0.2143473 0.6975848
## CM+TA-CM+SM     -0.03525178 -0.460227915 0.3897243 0.9999664
## HP+RS-CM+SM      0.02694276 -0.398033370 0.4519189 0.9999931
## HP+SM-CM+SM     -0.04916344 -0.474139568 0.3758127 0.9997649
## HP+TA-CM+SM     -0.20546754 -0.630443675 0.2195086 0.7204812
## HP+RS-CM+TA      0.06219454 -0.362781585 0.4871707 0.9990947
## HP+SM-CM+TA     -0.01391165 -0.438887783 0.4110645 0.9999999
## HP+TA-CM+TA     -0.17021576 -0.595191890 0.2547604 0.8576275
## HP+SM-HP+RS     -0.07610620 -0.501082328 0.3488699 0.9971991
## HP+TA-HP+RS     -0.23241030 -0.657386435 0.1925658 0.5971233
## HP+TA-HP+SM     -0.15630411 -0.581280237 0.2686720 0.8992901

Dropping the “weird” experimental run with SM+CM

par(mar=c(3,4.5,2,1),mfrow=c(2,1))
summ.dat.concat$Prey2 <- paste(summ.dat.concat$Blue.name,summ.dat.concat$Red.name,sep='+')
for(i in 1:dim(summ.dat.concat)[1]){
  if(summ.dat.concat$Prey[i]=='0'){summ.dat.concat$Prey2[i] <- '0'}
}
summ.dat.concat[summ.dat.concat$Prey2==0,]$Prey2 <- ' Starved '
summ.dat.concat$Description2 <- paste(summ.dat.concat$Description,summ.dat.concat$Prey,sep='.')
summ.dat.concat2 <- summ.dat.concat[summ.dat.concat$Description2!='MC_SM+CM.0',]

bargraph.CI(Prey2,Meso.mu, data=summ.dat.concat2[summ.dat.concat2$Meso.name=='MC',], las = 1, xlab='', ylab = expression(paste(italic('M. chamaeleon'),' growth rate (', d^{-1},')',sep='')), col = c('white',rep('gray60',6)), ylim =c(0,0.67),lc=F)
#TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat2[summ.dat.concat2$Meso.name=='MC',]))
text(x=seq(from=0.7,to=7.9,length.out=7),y=c(0.213,0.61,0.645,0.465,0.63,0.505,0.395),c('a','b','b','ab','b','b','ab'))
text(0.1,0.64,panel.labs[1,1],cex=1.2)

par(mar=c(4,4.5,1,1))
bargraph.CI(Prey2,Meso.mu.BV, data=summ.dat.concat2[summ.dat.concat2$Meso.name=='MC',], las = 1, xlab='Prey combination', ylab = '', col = c('white',rep('gray60',6)),ylim=c(0,.759),lc=F)
mtext(expression(paste(italic('M. chamaeleon'),' biovolume growth rate (', d^{-1},')',sep='')),side=2,line=3,cex=1)
#TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat2[summ.dat.concat2$Meso.name=='MC',]))
text(x=seq(from=0.7,to=7.9,length.out=7),y=c(0.265,0.63,0.707,0.595,0.742,0.598,0.458),c('a','b','b','b','b','b','ab'))
text(0.1,.73,panel.labs[1,2],cex=1.2)

TukeyHSD(aov(Meso.mu~Prey2,data=summ.dat.concat2[summ.dat.concat2$Meso.name=='MC',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.mu ~ Prey2, data = summ.dat.concat2[summ.dat.concat2$Meso.name == "MC", ])
## 
## $Prey2
##                        diff         lwr       upr     p adj
## CM+RS- Starved   0.39777525  0.12220531 0.6733452 0.0016978
## CM+SM- Starved   0.35783436  0.08226443 0.6334043 0.0053597
## CM+TA- Starved   0.25542793 -0.02014200 0.5309979 0.0822046
## HP+RS- Starved   0.34080679  0.06523686 0.6163767 0.0086714
## HP+SM- Starved   0.28845443  0.01288450 0.5640244 0.0359131
## HP+TA- Starved   0.15993051 -0.11563942 0.4355004 0.5246846
## CM+SM-CM+RS     -0.03994088 -0.39360125 0.3137195 0.9997851
## CM+TA-CM+RS     -0.14234731 -0.49600767 0.2113130 0.8509314
## HP+RS-CM+RS     -0.05696845 -0.41062881 0.2966919 0.9983706
## HP+SM-CM+RS     -0.10932081 -0.46298117 0.2443396 0.9517455
## HP+TA-CM+RS     -0.23784474 -0.59150510 0.1158156 0.3550214
## CM+TA-CM+SM     -0.10240643 -0.45606679 0.2512539 0.9645213
## HP+RS-CM+SM     -0.01702757 -0.37068793 0.3366328 0.9999986
## HP+SM-CM+SM     -0.06937993 -0.42304029 0.2842804 0.9951790
## HP+TA-CM+SM     -0.19790385 -0.55156421 0.1557565 0.5657989
## HP+RS-CM+TA      0.08537886 -0.26828150 0.4390392 0.9856039
## HP+SM-CM+TA      0.03302650 -0.32063386 0.3866869 0.9999290
## HP+TA-CM+TA     -0.09549742 -0.44915778 0.2581629 0.9747225
## HP+SM-HP+RS     -0.05235236 -0.40601272 0.3013080 0.9989869
## HP+TA-HP+RS     -0.18087628 -0.53453665 0.1727841 0.6612250
## HP+TA-HP+SM     -0.12852392 -0.48218429 0.2251364 0.9012991

TukeyHSD(aov(Meso.mu.BV~Prey2,data=summ.dat.concat2[summ.dat.concat2$Meso.name=='MC',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.mu.BV ~ Prey2, data = summ.dat.concat2[summ.dat.concat2$Meso.name == "MC", ])
## 
## $Prey2
##                        diff         lwr       upr     p adj
## CM+RS- Starved   0.37984947  0.07946743 0.6802315 0.0069885
## CM+SM- Starved   0.37468814  0.07430610 0.6750702 0.0079861
## CM+TA- Starved   0.33943636  0.03905432 0.6398184 0.0195190
## HP+RS- Starved   0.40163090  0.10124886 0.7020129 0.0039575
## HP+SM- Starved   0.32552470  0.02514267 0.6259067 0.0274812
## HP+TA- Starved   0.16922060 -0.13116144 0.4696026 0.5583193
## CM+SM-CM+RS     -0.00516133 -0.39066500 0.3803423 1.0000000
## CM+TA-CM+RS     -0.04041312 -0.42591678 0.3450906 0.9998606
## HP+RS-CM+RS      0.02178143 -0.36372224 0.4072851 0.9999964
## HP+SM-CM+RS     -0.05432477 -0.43982844 0.3311789 0.9992336
## HP+TA-CM+RS     -0.21062888 -0.59613254 0.1748748 0.5921030
## CM+TA-CM+SM     -0.03525178 -0.42075545 0.3502519 0.9999373
## HP+RS-CM+SM      0.02694276 -0.35856091 0.4124464 0.9999871
## HP+SM-CM+SM     -0.04916344 -0.43466711 0.3363402 0.9995668
## HP+TA-CM+SM     -0.20546754 -0.59097121 0.1800361 0.6187344
## HP+RS-CM+TA      0.06219454 -0.32330913 0.4476982 0.9983564
## HP+SM-CM+TA     -0.01391165 -0.39941532 0.3715920 0.9999997
## HP+TA-CM+TA     -0.17021576 -0.55571943 0.2152879 0.7898990
## HP+SM-HP+RS     -0.07610620 -0.46160987 0.3093975 0.9950117
## HP+TA-HP+RS     -0.23241030 -0.61791397 0.1530934 0.4808944
## HP+TA-HP+SM     -0.15630411 -0.54180778 0.2291996 0.8466628

# Setting figure panel margins
par(mar=c(1.5,2,1,1))

# Setting common y-axis limits
ylims <- matrix(c(0,.38,-.02,.65),nrow=2,ncol=2,byrow=T)

# Panel labeling
panel.labs <- matrix(c('(a)','(b)','(c)','(d)','(e)','(f)'),nrow=2,ncol=3,byrow=T)

# Setting y-axis labels
ylabels <- c(expression(paste('Attack rate (μL ',italic('M. rubrum')^-1,' ',d^-1,')')),expression(paste(italic('M. chamaeleon'), ' growth rate (',d^-1,')')))
ylabelcenter <- c(-.05,-.1)

# Using a fancy layout command to make the figure a little prettier
colsperpanel <- 3
rowsperpanel <- 3
layoutmatrix <- matrix(data=NA, nrow=2*rowsperpanel+1, ncol = 3*colsperpanel+1)
for(j in 1:length(Blue.cryptos)){
  for(k in 1:length(Red.cryptos)){
    layoutmatrix[((j-1)*rowsperpanel + 1):(j*rowsperpanel), ((k-1)*colsperpanel+2):(k*colsperpanel+1)] <- (j-1)*3+k
  }
}
layoutmatrix[,1] <- 7
layoutmatrix[dim(layoutmatrix)[1],] <- 8

layout(layoutmatrix)


for(i in 2){ # For M. chamaeleon only
  for(j in 1:length(Blue.cryptos)){ # For each blue cryptophyte, make a new row
    for(k in 1:length(Red.cryptos)){ # For each red cryptophyte, make a new panel
      # Subset the data
      subdat <- summ.dat.concat[summ.dat.concat$Meso.name==Mesos[i] & summ.dat.concat$Blue.name==Blue.cryptos[j] & summ.dat.concat$Red.name==Red.cryptos[k] & summ.dat.concat$Meso!='0',]
      # Make the relevant barplot
      if(dim(subdat)[1] > 0){
        bargraph.CI(Prey,Meso.mu,data=subdat,las=1,xlab='',names=c('Starved','Fed'),ylab='',col=c('white','gray60'), ylim = ylims[i,],lc=F)
        ttest <- t.test(subdat[subdat$Prey!='0',]$Meso.mu,subdat[subdat$Prey=='0',]$Meso.mu) 
        text(0.05,ylims[i,2]*.95,paste(panel.labs[j,k],Blue.cryptos[j],'+',Red.cryptos[k]),pos=4)
        if(ttest$p.value < 0.001){
          text(1.3, max(c(subdat$Meso.mu),na.rm=T), '***',cex=1.5)
        } else{
          if(ttest$p.value < 0.01){
            text(1.3, max(c(subdat$Meso.mu),na.rm=T),'**',cex=1.5)
          } else{
            if(ttest$p.value < 0.05){
              text(1.3, max(c(subdat$Meso.mu),na.rm=T),'*',cex=1.5)
            }else{
              if(ttest$p.value < 0.056){
                text(1.3, max(c(subdat$Meso.mu),na.rm=T),'.',cex=1.5)
              } 
            }
          }
        }
      } else{
        # If there are no data, create a blank plot as a placeholder
        plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty='n')
      }
      if(j==2){ if(k == 2){mtext('Feeding treatment',side=1,line=2.5,cex=.7)}}
      if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.5,cex=.7, at = ylabelcenter[i])}}
    }
    
  }
  # Blank plots as placeholders
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='',bty='n')
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty = 'n')
}

# Setting figure panel margins
par(mar=c(1.5,2,1,1))

# Setting common y-axis limits
ylims <- matrix(c(0,.38,-.02,.75),nrow=2,ncol=2,byrow=T)

# Panel labeling
panel.labs <- matrix(c('(a)','(b)','(c)','(d)','(e)','(f)'),nrow=2,ncol=3,byrow=T)

# Setting y-axis labels
ylabels <- c(expression(paste('Attack rate (μL ',italic('M. rubrum')^-1,' ',d^-1,')')),expression(paste(italic('M. chamaeleon'),' biovolume growth rate (',d^-1,')')))
ylabelcenter <- c(-.05,-.1)

# Using a fancy layout command to make the figure a little prettier
colsperpanel <- 3
rowsperpanel <- 3
layoutmatrix <- matrix(data=NA, nrow=2*rowsperpanel+1, ncol = 3*colsperpanel+1)
for(j in 1:length(Blue.cryptos)){
  for(k in 1:length(Red.cryptos)){
    layoutmatrix[((j-1)*rowsperpanel + 1):(j*rowsperpanel), ((k-1)*colsperpanel+2):(k*colsperpanel+1)] <- (j-1)*3+k
  }
}
layoutmatrix[,1] <- 7
layoutmatrix[dim(layoutmatrix)[1],] <- 8

layout(layoutmatrix)


for(i in 2){ # For M. chamaeleon only
  for(j in 1:length(Blue.cryptos)){ # For each blue cryptophyte, make a new row
    for(k in 1:length(Red.cryptos)){ # For each red cryptophyte, make a new panel
      # Subset the data
      subdat <- summ.dat.concat[summ.dat.concat$Meso.name==Mesos[i] & summ.dat.concat$Blue.name==Blue.cryptos[j] & summ.dat.concat$Red.name==Red.cryptos[k] & summ.dat.concat$Meso!='0',]
      # Make the relevant barplot
      if(dim(subdat)[1] > 0){
        bargraph.CI(Prey,Meso.mu.BV,data=subdat,las=1,xlab='',names=c('Starved','Fed'),ylab='',col=c('white','gray60'), ylim = ylims[i,],lc=F)
        ttest <- t.test(subdat[subdat$Prey!='0',]$Meso.mu.BV,subdat[subdat$Prey=='0',]$Meso.mu.BV) 
        text(0.05,ylims[i,2]*.95,paste(panel.labs[j,k],Blue.cryptos[j],'+',Red.cryptos[k]),pos=4)
        if(ttest$p.value < 0.001){
          text(1.3, max(c(subdat$Meso.mu.BV),na.rm=T), '***',cex=1.5)
        } else{
          if(ttest$p.value < 0.01){
            text(1.3, max(c(subdat$Meso.mu.BV),na.rm=T),'**',cex=1.5)
          } else{
            if(ttest$p.value < 0.05){
              text(1.3, max(c(subdat$Meso.mu.BV),na.rm=T),'*',cex=1.5)
            } else{
            if(ttest$p.value < 0.056){
              text(1.3, max(c(subdat$Meso.mu.BV),na.rm=T),'.',cex=1.5)
            } }
          }
        }
      } else{
        # If there are no data, create a blank plot as a placeholder
        plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty='n')
      }
      if(j==2){ if(k == 2){mtext('Feeding treatment',side=1,line=2.5,cex=.7)}}
      if(j==1){ if(k == 1){mtext(ylabels[i],side=2,line=2.5,cex=.7, at = ylabelcenter[i])}}
    }
    
  }
  # Blank plots as placeholders
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='',bty='n')
  plot(0,0,type='n',xaxt='n',yaxt='n',xlab='',ylab='', bty = 'n')
}

Gross growth efficiency

Load carbon per cell data

# Compiling carbon contents, in ng C per cell
# From Peltomaa & Johnson
Tam.CpermL <- c(55,124,285,634,1120,2540)
Tam.cellspermL <- c(600,1500,3200,7000,12500,28000)
TA.Cpercell <- mean(Tam.CpermL / Tam.cellspermL, na.rm=T)

# From Paight et al
CN.data.paight <- read.csv("/Users/hollyvm/GoogleSync/Mesodinium/Chamaeleon_4preyTranscriptomes/Mchamaeleon_4prey_Sept2020 - PhysiolData.csv")
MC.Cpercell <- mean(CN.data.paight[CN.data.paight$Fed =='Y',]$ngC_cell,na.rm=T)
CM.Cpercell <- mean(CN.data.paight[CN.data.paight$Fed=='Prey' & CN.data.paight$Prey=='CM',]$ngC_cell,na.rm=T)
HP.Cpercell <- mean(CN.data.paight[CN.data.paight$Fed=='Prey' & CN.data.paight$Prey=='HP',]$ngC_cell,na.rm=T)
RS.Cpercell <- mean(CN.data.paight[CN.data.paight$Fed=='Prey' & CN.data.paight$Prey=='RS',]$ngC_cell,na.rm=T)
SM.Cpercell <- mean(CN.data.paight[CN.data.paight$Fed=='Prey' & CN.data.paight$Prey=='SM',]$ngC_cell,na.rm=T)

# From Stoecker et al 1991
MR.Cpercell <- mean(c(0.685, 0.542, 0.621))

Compute gross growth efficiency

summ.dat.concat$Meso.Cpercell <- MR.Cpercell
summ.dat.concat[summ.dat.concat$Meso.name=='MC',]$Meso.Cpercell <- MC.Cpercell
summ.dat.concat$Red.Cpercell <- TA.Cpercell
summ.dat.concat[summ.dat.concat$Red.name=='RS',]$Red.Cpercell <- RS.Cpercell
summ.dat.concat[summ.dat.concat$Red.name=='SM',]$Red.Cpercell <- SM.Cpercell
summ.dat.concat$Blue.Cpercell <- HP.Cpercell
summ.dat.concat[summ.dat.concat$Blue.name=='CM',]$Blue.Cpercell <- CM.Cpercell

# Ingestion units: number of cryptophytes consumed per Mesodinium per day 
# i * ng C per cell = ng C of cryptophytes consumed per Mesodinium cell per day
summ.dat.concat$C.ingest <- summ.dat.concat$i.Red*summ.dat.concat$Red.Cpercell + summ.dat.concat$i.Blue*summ.dat.concat$Blue.Cpercell

# Carbon per carbon ingestion, in units of per day
summ.dat.concat$C.ingest.perMesoC <- summ.dat.concat$C.ingest/summ.dat.concat$Meso.Cpercell

# Gross growth efficiency, unitless (growth rate is per day, ingestion is per day)
summ.dat.concat$Meso.GGE <- summ.dat.concat$Meso.mu/summ.dat.concat$C.ingest.perMesoC

Plot gross growth efficiency

par(mar=c(2,4,3,1),mfrow=c(2,1))
bargraph.CI(Prey2,Meso.GGE,data=summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char=='MR',],col='gray60',las=1,xlab='',ylab=expression(paste(italic('M. rubrum'),' GGE')),ylim=c(0,9.1),lc=F)
text(x=seq(from = 0.7, to = 6.7, length.out=6),y=c(8.9,4.3,3.4,5.8,3.5,1.8),rep('a',6))
text(0.2,8.7,'(a)')

par(mar=c(4,4,1,1))
bargraph.CI(Prey2,Meso.GGE,data=summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char=='MC',],las=1,xlab='Prey Treatment', ylab= expression(paste(italic('M. chamaeleon'),' GGE')),col='gray60', ylim=c(0,6.8),lc=F)
text(x=seq(from = 0.7, to = 6.7, length.out=6),y=c(5.7,1.15,1.6,6.4,4,1.2),c('a','b','b','a','ab','b'))
text(0.2,6.2,'(b)')

TukeyHSD(aov(Meso.GGE~Prey2,data=summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char=='MR',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.GGE ~ Prey2, data = summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char == "MR", ])
## 
## $Prey2
##                    diff        lwr      upr     p adj
## CM+SM-CM+RS -2.72138851 -11.483651 6.040874 0.8874926
## CM+TA-CM+RS -2.79935668 -10.636562 5.037849 0.8197929
## HP+RS-CM+RS -1.60125406  -9.438460 6.235952 0.9785070
## HP+SM-CM+RS -2.69998481 -10.537190 5.137221 0.8396868
## HP+TA-CM+RS -4.00401197 -11.841218 3.833194 0.5344277
## CM+TA-CM+SM -0.07796817  -8.840230 8.684294 1.0000000
## HP+RS-CM+SM  1.12013445  -7.642128 9.882397 0.9974251
## HP+SM-CM+SM  0.02140370  -8.740859 8.783666 1.0000000
## HP+TA-CM+SM -1.28262346 -10.044886 7.479639 0.9951573
## HP+RS-CM+TA  1.19810262  -6.639103 9.035308 0.9940868
## HP+SM-CM+TA  0.09937187  -7.737834 7.936578 1.0000000
## HP+TA-CM+TA -1.20465529  -9.041861 6.632550 0.9939371
## HP+SM-HP+RS -1.09873075  -8.935936 6.738475 0.9960354
## HP+TA-HP+RS -2.40275791 -10.239964 5.434448 0.8925584
## HP+TA-HP+SM -1.30402716  -9.141233 6.533179 0.9913058

TukeyHSD(aov(Meso.GGE~Prey2,data=summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char=='MC',]))

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Meso.GGE ~ Prey2, data = summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char == "MC", ])
## 
## $Prey2
##                   diff        lwr        upr     p adj
## CM+SM-CM+RS -4.3626533 -7.6469860 -1.0783207 0.0078183
## CM+TA-CM+RS -3.8968495 -7.1811821 -0.6125168 0.0173802
## HP+RS-CM+RS -0.3956663 -3.6799989  2.8886664 0.9982251
## HP+SM-CM+RS -1.7293572 -5.0136899  1.5549754 0.5174895
## HP+TA-CM+RS -4.2053072 -7.4896398 -0.9209745 0.0102257
## CM+TA-CM+SM  0.4658039 -2.8185288  3.7501365 0.9961737
## HP+RS-CM+SM  3.9669871  0.6826544  7.2513198 0.0153997
## HP+SM-CM+SM  2.6332961 -0.6510365  5.9176288 0.1477904
## HP+TA-CM+SM  0.1573462 -3.1269865  3.4416788 0.9999804
## HP+RS-CM+TA  3.5011832  0.2168505  6.7855159 0.0344338
## HP+SM-CM+TA  2.1674922 -1.1168404  5.4518249 0.2979408
## HP+TA-CM+TA -0.3084577 -3.5927904  2.9758750 0.9994631
## HP+SM-HP+RS -1.3336910 -4.6180236  1.9506417 0.7462818
## HP+TA-HP+RS -3.8096409 -7.0939736 -0.5253082 0.0202056
## HP+TA-HP+SM -2.4759500 -5.7602826  0.8083827 0.1891240

Growth vs ingestion

# Plot
par(mar=c(4,4,1,1),mfrow=c(1,1))
plot(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$C.ingest.perMesoC,summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Meso.mu, xlim=c(0,1.6), ylim=c(0,1.6), type='n', xlab = '', ylab = '',las=1 )
mtext(expression(paste('Ingestion (',C[prey], ' · ',C[italic('Meso')]^-1,' · ',d^-1,')')),side=1,line=2.5)
mtext(expression(paste('Growth (',d^-1,')')), side = 2, line = 2.5)
abline(a=0,b=1)
points(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$C.ingest.perMesoC,summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Meso.mu, pch = c(21,22,24)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Red.name)], bg = c('white','black')[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Blue.name)], cex = c(1.5,1.5,1.2)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Red.name)])

points(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$C.ingest.perMesoC,summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Meso.mu, pch = c(21,22,24)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Red.name)], col = HPcol, bg = c('white',HPcol)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Blue.name)], cex = c(1.5,1.5,1.2)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Red.name)])
#abline(a=0,b=1)

legend(x = 'topleft', inset = c(0.01,0.01), legend = c('RS + CM', 'SM + CM','TA + CM', 'RS + HP','SM + HP','TA + HP'), pch=c(21,22,24,21,22,24), pt.cex=c(1.5,1.5,1.2,1.5,1.5,1.2), pt.bg = c('white','white','white','black','black','black'),cex=.8)

Combining growth vs ingestion and GGE figures

# Plot
par(mar=c(4,4,1,1))
layout(matrix(c(1,1,1,2,2,2,2,1,1,1,3,3,3,3),nrow=2,ncol=7,byrow=T))
plot(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$C.ingest.perMesoC,summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Meso.mu, xlim=c(0,1.6), ylim=c(0,1.6), type='n', xlab = '', ylab = '',las=1 )
mtext(expression(paste('Ingestion (',C[prey], ' · ',C[italic('Meso')]^-1,' · ',d^-1,')')),side=1,line=2.5,cex=.7)
mtext(expression(paste('Growth (',d^-1,')')), side = 2, line = 2.3,cex=.7)
abline(a=0,b=1)
points(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$C.ingest.perMesoC,summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Meso.mu, pch = c(21,22,24)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Red.name)], bg = c('white','black')[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Blue.name)], cex = c(1.5,1.5,1.2)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]$Red.name)])

points(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$C.ingest.perMesoC,summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Meso.mu, pch = c(21,22,24)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Red.name)], col = HPcol, bg = c('white',HPcol)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Blue.name)], cex = c(1.5,1.5,1.2)[as.factor(summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]$Red.name)])
#abline(a=0,b=1)

legend(x = 'topright', inset = c(0.01,0.01), legend = c('CM + RS', 'CM + SM','CM + TA', 'HP + RS','HP + SM','HP + TA'), pch=c(21,22,24,21,22,24), pt.cex=c(1.5,1.5,1.2,1.5,1.5,1.2), pt.bg = c('white','white','white','black','black','black'),cex=1)
text(0.05,1.55,'(a)')


par(mar=c(3,3,1,1))
bargraph.CI(Prey2,Meso.GGE,data=summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char=='MR',],col='gray60',las=1,xlab='',ylab='',ylim=c(0,9.2),lc=F)
text(x=seq(from = 0.7, to = 6.7, length.out=6),y=c(8.9,4.3,3.4,5.8,3.5,1.8),rep('a',6))
text(0.2,8.7,'(b)')
mtext(expression(paste(italic('M. rubrum'),' GGE')),side=2, line = 2., cex=0.7)

par(mar=c(4,3,0,1))
bargraph.CI(Prey2,Meso.GGE,data=summ.dat.concat[!is.na(summ.dat.concat$Meso.GGE) & summ.dat.concat$Meso.2char=='MC',],las=1,xlab='', ylab= '',col='gray60', ylim=c(0,6.8),lc=F)
text(x=seq(from = 0.7, to = 6.7, length.out=6),y=c(5.7,1.15,1.6,6.4,4,1.2),c('a','b','b','a','ab','b'))
text(0.2,6.2,'(c)')
mtext(expression(paste(italic('M. chamaeleon'),' GGE')),side=2, line = 2., cex=0.7)
mtext('Prey Treatment',side=1,line=2.5,cex=0.7)

Adding ingestion rates from other papers

#head(meta.dat)
par(mar=c(4,4,1,1),mfrow=c(1,2))

# M. rubrum
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MR',]
MR.prey.pchs <- c(25,6)
MR.prey.cols <- c(TAcol,TAcol)
MR.prey.bgs <- c(TAcol.bg, TAcol.bg)
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MR.prey.pchs[as.factor(subdat$Prey.species.)], col = MR.prey.cols[as.factor(subdat$Prey.species.)], bg = MR.prey.bgs[as.factor(subdat$Prey.species.)], las = 1, xlab='Prey concentration (cells/mL)', ylab = 'Ingestion rate (prey per Meso per day)', ylim=c(0,10))

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]
points(subdat2$Red.pop.72, subdat2$i.Red, pch = c(22,24,25)[as.factor(subdat2$Red.name)], cex=1.3,lwd=2)
points(subdat2$Blue.pop.72, subdat2$i.Blue, pch = c(21,23)[as.factor(subdat2$Blue.name)], cex=1.3,lwd=2)

# M. chamaeleon
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MC',]
MC.prey.pchs <- c(21,23,22,24,25)
MC.prey.cols <- c(HPcol,HPcol,TAcol,TAcol,TAcol)
MC.prey.bgs <- c(HPcol.bg, HPcol.bg, TAcol.bg, TAcol.bg, TAcol.bg)
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MC.prey.pchs[as.factor(subdat$Prey.species.)], col = MC.prey.cols[as.factor(subdat$Prey.species.)], bg = MC.prey.bgs[as.factor(subdat$Prey.species.)], las = 1, xlab='Prey concentration (cells/mL)', ylab = 'Ingestion rate (prey per Meso per day)', ylim=c(0,15),xlim=c(0,30000))

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]
points(subdat2$Red.pop.72, subdat2$i.Red, pch = c(22,24,25)[as.factor(subdat2$Red.name)], cex=1.3,lwd=2)
points(subdat2$Blue.pop.72, subdat2$i.Blue, pch = c(21,23)[as.factor(subdat2$Blue.name)], cex=1.3,lwd=2)

Let’s try graying out old data and plotting new data in color

Supplemental Figure 5

#head(meta.dat)
par(mar=c(4,4,1,1),mfrow=c(1,2))

# M. rubrum
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MR',]
MR.prey.pchs <- c(24,25,25)
MR.prey.cols <- c(TAcol,TAcol)
MR.prey.bgs <- c(TAcol.bg, TAcol.bg)
MR.prey.cols <- c('black','black','black')
MR.prey.bgs <- c('gray70','gray70', 'gray70')
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MR.prey.pchs[as.factor(subdat$Prey.species.)], col = MR.prey.cols[as.factor(subdat$Prey.species.)], bg = MR.prey.bgs[as.factor(subdat$Prey.species.)], las = 1, xlab='', ylab = '', ylim=c(0,8),xlim=c(0,15200))
mtext(expression(paste('Prey concentration (cells · ',mL^-1,')')), side = 1, line = 2.5)
mtext('Ingestion rate',side=2,line=3.2)
mtext(expression(paste('(Cryptophytes · ', italic('M. rubrum')^-1, ' · ', d^-1,')')), side = 2, line = 2)
text(200,7.9,'(a)')

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]
points(subdat2$Red.pop.72, subdat2$i.Red, pch = c(21,23,25)[as.factor(subdat2$Red.name)], col = c(TAcol,TAcol,TAcol)[as.factor(subdat2$Red.name)], bg = c(TAcol.bg,TAcol.bg,TAcol.bg)[as.factor(subdat2$Red.name)], cex=c(1.7,1.7,1.5)[as.factor(subdat2$Red.name)],lwd=2)
points(subdat2$Blue.pop.72, subdat2$i.Blue, pch = c(22,24)[as.factor(subdat2$Blue.name)], col = HPcol, bg = HPcol.bg, cex=c(1.5,1.7)[as.factor(subdat2$Blue.name)],lwd=2)

# M. chamaeleon
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MC',]
MC.prey.pchs <- c(22,24,21,23,25)
MC.prey.cols <- rep('black',5)
MC.prey.bgs <- c('white','white',rep('gray70',3))
MC.prey.bgs <- rep('gray70',5)
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MC.prey.pchs[as.factor(subdat$Prey.species.)], col = MC.prey.cols[as.factor(subdat$Prey.species.)], bg = MC.prey.bgs[as.factor(subdat$Prey.species.)], cex = c(1.2,1,1.2,1.2,1)[as.factor(subdat$Prey.species.)], las = 1, xlab='', ylab = '', ylim=c(0,15),xlim=c(0,30000))
mtext(expression(paste('Prey concentration (cells · ',mL^-1,')')), side = 1, line = 2.5)
mtext('Ingestion rate',side=2,line=3.2)
mtext(expression(paste('(Cryptophytes · ', italic('M. chamaeleon')^-1, ' · ', d^-1,')')), side = 2, line = 2)
text(500,14.8,'(b)')

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]
subdat3 <- as.data.frame(cbind(subdat2$Red.pop.72,subdat2$i.Red,subdat2$Red.name))
colnames(subdat3) <- c('Abundance','Ingestion','Name')
subdat4 <- as.data.frame(cbind(subdat2$Blue.pop.72,subdat2$i.Blue,subdat2$Blue.name))
colnames(subdat4) <- c('Abundance','Ingestion','Name')
subdat3 <- rbind(subdat3,subdat4)
subdat3$ordering <- runif(dim(subdat3)[1])
subdat3 <- subdat3[order(subdat3$ordering), ]

points(subdat3$Abundance,subdat3$Ingestion,pch = c(22,24,21,23,25)[as.factor(subdat3$Name)], col = c(HPcol,HPcol,TAcol,TAcol,TAcol)[as.factor(subdat3$Name)], bg = c(HPcol.bg,HPcol.bg,TAcol.bg,TAcol.bg,TAcol.bg)[as.factor(subdat3$Name)], cex = c(1.5,1.7,1.7,1.7,1.5)[as.factor(subdat3$Name)], lwd = 2)

legend(x='bottomright',inset=c(0.01,0.01), legend=c('CM','HP','RS','SM','TA'),pch=c(22,24,21,23,25),col=c(HPcol,HPcol, TAcol,TAcol,TAcol), pt.bg= c(HPcol.bg,HPcol.bg,TAcol.bg,TAcol.bg,TAcol.bg),pt.cex=c(1.7,1.5,1.7,1.7,1.5),lwd=2,lty=0)

Supplemental Figure 6

#head(meta.dat)
par(mar=c(4,4,1,1),mfrow=c(1,2))

# M. rubrum
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MR',]
MR.prey.pchs <- c(24,25,25)
MR.prey.cols <- c('black','black','black')
MR.prey.bgs <- c('gray70','gray70', 'gray70')
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MR.prey.pchs[as.factor(subdat$Prey.species.)], col = MR.prey.cols[as.factor(subdat$Prey.species.)], bg = MR.prey.bgs[as.factor(subdat$Prey.species.)], las = 1, xlab='', ylab = '', ylim=c(0,8),xlim=c(0,30000))
mtext(expression(paste('Prey concentration (cells · ',mL^-1,')')), side = 1, line = 2.5)
mtext('Ingestion rate',side=2,line=3.2)
mtext(expression(paste('(Cryptophytes · ', italic('M. rubrum')^-1, ' · ', d^-1,')')), side = 2, line = 2)
text(500,7.9,'(a)')

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MR' & summ.dat.concat$Prey!='0',]
subdat2$i.tot <- subdat2$i.Red+subdat2$i.Blue
subdat2$Tot.pop.72 <- subdat2$Red.pop.72+subdat2$Blue.pop.72
prey.pchs <- c(21,21,22,22,24,24)
prey.bgs <- c('white',TAcol.bg,'white',TAcol.bg,'white',TAcol.bg)
prey.cex <- c(1.7,1.7,1.7,1.7,1.5,1.5)
points(subdat2$Tot.pop.72, subdat2$i.tot,pch=prey.pchs[as.factor(subdat2$Prey)],bg=prey.bgs[as.factor(subdat2$Prey)], col=TAcol, cex=prey.cex[as.factor(subdat2$Prey)],lwd=2)

legend(x = 'topright', inset = c(0.01,0.01), legend = c('RS + CM', 'SM + CM','TA + CM', 'RS + HP','SM + HP','TA + HP'), pch=c(21,22,24,21,22,24),lty=0, lwd=2, pt.cex=c(1.7,1.7,1.5,1.7,1.7,1.5), pt.bg = c('white','white','white',TAcol.bg,TAcol.bg,TAcol.bg),cex=0.9,col=TAcol)


# M. chamaeleon
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MC',]
MC.prey.pchs <- c(22,24,21,23,25)
MC.prey.cols <- rep('black',5)
MC.prey.bgs <- c('white','white',rep('gray70',3))
MC.prey.bgs <- rep('gray70',5)
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MC.prey.pchs[as.factor(subdat$Prey.species.)], col = MC.prey.cols[as.factor(subdat$Prey.species.)], bg = MC.prey.bgs[as.factor(subdat$Prey.species.)], cex = c(1.2,1,1.2,1.2,1)[as.factor(subdat$Prey.species.)], las = 1, xlab='', ylab = '', ylim=c(0,15),xlim=c(0,30000))
mtext(expression(paste('Prey concentration (cells · ',mL^-1,')')), side = 1, line = 2.5)
mtext('Ingestion rate',side=2,line=3.2)
mtext(expression(paste('(Cryptophytes · ', italic('M. chamaeleon')^-1, ' · ', d^-1,')')), side = 2, line = 2)
text(500,14.8,'(b)')

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MC' & summ.dat.concat$Prey!='0',]
subdat2$i.tot <- subdat2$i.Red+subdat2$i.Blue
subdat2$Tot.pop.72 <- subdat2$Red.pop.72+subdat2$Blue.pop.72
prey.pchs <- c(21,21,22,22,24,24)
prey.bgs <- c('white',HPcol.bg,'white',HPcol.bg,'white',HPcol.bg)
prey.cex <- c(1.7,1.7,1.7,1.7,1.5,1.5)
points(subdat2$Tot.pop.72, subdat2$i.tot,pch=prey.pchs[as.factor(subdat2$Prey)],bg=prey.bgs[as.factor(subdat2$Prey)], col=HPcol, cex=prey.cex[as.factor(subdat2$Prey)],lwd=2)

legend(x = 'bottomright', inset = c(0.01,0.01), legend = c('RS + CM', 'SM + CM','TA + CM', 'RS + HP','SM + HP','TA + HP'), pch=c(21,22,24,21,22,24),lty=0, lwd=2, pt.cex=c(1.7,1.7,1.5,1.7,1.7,1.5), pt.bg = c('white','white','white',HPcol.bg,HPcol.bg,HPcol.bg),cex=0.9,col=HPcol)

Supplemental Figure 7

#head(meta.dat)
par(mar=c(4,4,1,1),mfrow=c(1,2))

# M. rubrum
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MR',]
MR.prey.pchs <- c(24,23,22,21)
MR.prey.cols <- c('black','black','black','black')
MR.prey.bgs <- c('gray70','gray70', 'gray70', 'gray70')
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MR.prey.pchs[as.factor(subdat$Refrence.)], col = MR.prey.cols[as.factor(subdat$Refrence.)], bg = MR.prey.bgs[as.factor(subdat$Refrence.)], las = 1, xlab='', ylab = '', ylim=c(0,8),xlim=c(0,15200),cex=1.2)
mtext(expression(paste('Prey concentration (cells · ',mL^-1,')')), side = 1, line = 2.5)
mtext('Ingestion rate',side=2,line=3.2)
mtext(expression(paste('(Cryptophytes · ', italic('M. rubrum')^-1, ' · ', d^-1,')')), side = 2, line = 2)
text(200,7.9,'(a)')
legend(x='bottomright', inset=c(0.01,0.01), legend=c('Yih et al. 2004','Smith & Hansen 2007','Peltomaa & Johnson 2017','Moeller et al. 2024'),pch=c(21,22,23,24),pt.bg='gray70',cex=.75,pt.cex=1.2)


# M. chamaeleon
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MC',]
MC.prey.pchs <- c(21,22)
MC.prey.cols <- rep('black',5)
MC.prey.bgs <- rep('gray70',5)
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MC.prey.pchs[as.factor(subdat$Refrence.)], col = MC.prey.cols[as.factor(subdat$Refrence.)], bg = MC.prey.bgs[as.factor(subdat$Refrence.)], cex = 1.2, las = 1, xlab='', ylab = '', ylim=c(0,15),xlim=c(0,30000))
mtext(expression(paste('Prey concentration (cells · ',mL^-1,')')), side = 1, line = 2.5)
mtext('Ingestion rate',side=2,line=3.2)
mtext(expression(paste('(Cryptophytes · ', italic('M. chamaeleon')^-1, ' · ', d^-1,')')), side = 2, line = 2)
text(500,14.8,'(b)')
legend(x='bottomright', inset=c(0.01,0.01), legend=c('Moeller & Johnson 2018','Moeller et al. 2021'),pch=c(21,22),pt.bg='gray70',cex=.75,pt.cex=1.2)

Log-scale

#head(meta.dat)
par(mar=c(4,4,1,1),mfrow=c(1,2))

# M. rubrum
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MR',]
MR.prey.pchs <- c(25,25)
MR.prey.cols <- c(TAcol,TAcol)
MR.prey.bgs <- c(TAcol.bg, TAcol.bg)
MR.prey.cols <- c('black','black')
MR.prey.bgs <- c('gray50', 'gray50')
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MR.prey.pchs[as.factor(subdat$Prey.species.)], col = MR.prey.cols[as.factor(subdat$Prey.species.)], bg = MR.prey.bgs[as.factor(subdat$Prey.species.)], las = 1, xlab='Prey concentration (cells/mL)', ylab = 'Ingestion rate (prey per Meso per day)', ylim=c(.050,5.),xlim=c(300,13500),log='xy')

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]
points(subdat2$Red.pop.72, subdat2$i.Red, pch = c(21,23,25)[as.factor(subdat2$Red.name)], col = c(TAcol,TAcol,TAcol)[as.factor(subdat2$Red.name)], bg = c(TAcol.bg,TAcol.bg,TAcol.bg)[as.factor(subdat2$Red.name)], cex=c(1.7,1.7,1.5)[as.factor(subdat2$Red.name)],lwd=2)
points(subdat2$Blue.pop.72, subdat2$i.Blue, pch = c(22,24)[as.factor(subdat2$Blue.name)], col = HPcol, bg = HPcol.bg, cex=c(1.5,1.7)[as.factor(subdat2$Blue.name)],lwd=2)

# M. chamaeleon
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MC',]
MC.prey.pchs <- c(22,24,21,23,25)
MC.prey.cols <- rep('black',5)
MC.prey.bgs <- c('white','white',rep('gray70',3))
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MC.prey.pchs[as.factor(subdat$Prey.species.)], col = MC.prey.cols[as.factor(subdat$Prey.species.)], bg = MC.prey.bgs[as.factor(subdat$Prey.species.)], cex = c(1.2,1,1.2,1.2,1)[as.factor(subdat$Prey.species.)], las = 1, xlab='Prey concentration (cells/mL)', ylab = 'Ingestion rate (prey per Meso per day)', ylim=c(0.2,18),xlim=c(100,32000),log='xy')

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]
points(subdat2$Red.pop.72, subdat2$i.Red, pch = c(21,23,25)[as.factor(subdat2$Red.name)], col = c(TAcol,TAcol,TAcol)[as.factor(subdat2$Red.name)], bg = c(TAcol.bg,TAcol.bg,TAcol.bg)[as.factor(subdat2$Red.name)], cex=c(1.7,1.7,1.5)[as.factor(subdat2$Red.name)],lwd=2)
points(subdat2$Blue.pop.72, subdat2$i.Blue, pch = c(22,24)[as.factor(subdat2$Blue.name)], col = HPcol, bg = HPcol.bg, cex=c(1.5,1.7)[as.factor(subdat2$Blue.name)],lwd=2)

legend(x='bottomright',inset=c(0.01,0.01), legend=c('CM','HP','RS','SM','TA'),pch=c(22,24,21,23,25),col=c(HPcol,HPcol, TAcol,TAcol,TAcol), pt.bg= c(HPcol.bg,HPcol.bg,TAcol.bg,TAcol.bg,TAcol.bg),pt.cex=c(1.7,1.5,1.7,1.7,1.5),lwd=2,lty=0)

Let’s try graying out the other data except for “preferred” prey

#head(meta.dat)
par(mar=c(4,4,1,1),mfrow=c(1,2))

# M. rubrum
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MR',]
MR.prey.pchs <- c(25,25)
MR.prey.cols <- c(TAcol,TAcol)
MR.prey.bgs <- c(TAcol.bg, TAcol.bg)
#MR.prey.cols <- c('black','black')
#MR.prey.bgs <- c('gray50', 'gray50')
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MR.prey.pchs[as.factor(subdat$Prey.species.)], col = MR.prey.cols[as.factor(subdat$Prey.species.)], bg = MR.prey.bgs[as.factor(subdat$Prey.species.)], las = 1, xlab='Prey concentration (cells/mL)', ylab = 'Ingestion rate (prey per Meso per day)', ylim=c(0,5.),xlim=c(0,13500))

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MR',]
MR.prey.cols <- c('black','black',TAcol)
MR.prey.bgs <- c('white','white', TAcol.bg)
points(subdat2$Red.pop.72, subdat2$i.Red, pch = c(22,24,25)[as.factor(subdat2$Red.name)], col = MR.prey.cols[as.factor(subdat2$Red.name)], bg= MR.prey.bgs[as.factor(subdat2$Red.name)], cex=1.5,lwd=2)
MR.prey.cols <- c('black',HPcol)
MR.prey.bgs <- c('white',HPcol.bg)
points(subdat2$Blue.pop.72, subdat2$i.Blue, pch = c(21,23)[as.factor(subdat2$Blue.name)], col = MR.prey.cols[as.factor(subdat2$Blue.name)], bg= MR.prey.bgs[as.factor(subdat2$Blue.name)],cex=1.5,lwd=2)

# M. chamaeleon
subdat <- meta.dat[meta.dat$Mesodinium.Species.=='MC',]
MC.prey.pchs <- c(21,23,22,24,25)
MC.prey.cols <- c(HPcol,HPcol,TAcol,TAcol,'black')
MC.prey.bgs <- c(HPcol.bg, HPcol.bg, TAcol.bg, TAcol.bg, 'white')
plot(subdat$Prey.concentration..cells.mL, subdat$Ingestion.rate...prey..meso.day., pch = MC.prey.pchs[as.factor(subdat$Prey.species.)], col = MC.prey.cols[as.factor(subdat$Prey.species.)], bg = MC.prey.bgs[as.factor(subdat$Prey.species.)], las = 1, xlab='Prey concentration (cells/mL)', ylab = 'Ingestion rate (prey per Meso per day)', ylim=c(0,15),xlim=c(0,30000))

subdat2 <- summ.dat.concat[summ.dat.concat$Meso.2char=='MC',]
points(subdat2$Red.pop.72, subdat2$i.Red, pch = c(22,24,25)[as.factor(subdat2$Red.name)], col = c(TAcol,TAcol,'black')[as.factor(subdat2$Red.name)], bg = c(TAcol.bg,TAcol.bg,'white')[as.factor(subdat2$Red.name)], cex=1.3,lwd=2)
points(subdat2$Blue.pop.72, subdat2$i.Blue, pch = c(21,23)[as.factor(subdat2$Blue.name)], col = HPcol, bg = HPcol.bg, cex=1.3,lwd=2)